requirements.txt

gradio
transformers
datasets
Pillow
torch

app.py

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+import gradio as gr
+from transformers import AutoModelForImageClassification, AutoProcessor, pipeline
+from datasets import load_dataset
+from PIL import Image
+import torch
+
+# Load the model and processor from Hugging Face
+model_name = "Deepri24/my_awesome_emotion_identifier_model"
+processor = AutoProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+# Instantiate a pipeline for image classification
+classifier = pipeline("image-classification", model=model_name)
+
+def predict(image):
+    # Use the classifier pipeline to get predictions
+    results = classifier(image)
+    
+    # Extract the label from the results
+    predicted_label = results[0]['label']  # Get the top prediction
+    
+    return predicted_label
+
+# Load the validation split of the dataset but only the first 10 samples
+ds = load_dataset('FastJobs/Visual_Emotional_Analysis', split="train[:10]")
+
+# Define a function to get sample images
+def get_samples():
+    # Load two sample images from the dataset
+    sample_images = [ds["image"][i] for i in [0, 1]]  # Get the first two images 
+    return sample_images
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),  # Accept PIL images
+    outputs="text",  # Output will be a text label
+    title="Emotion Identifier",
+    description="Upload an image to identify the emotion.",
+    examples=get_samples()  # Use sample images for example inputs
+)
+
+# Launch the interface
+interface.launch()